Evidence map›Paper›PMID 41491535›Full record

ArticleJournal of intensive care2026

Exploratory characterization of dynamic soluble programmed death-ligand 1 trajectories and their association with mortality in critical coronavirus disease 2019.

Shungo Takeuchi, Eiji Kawamoto, Takashi Matsusaki, Daisuke Ono, Yosuke Sakakura, Arong Gaowa, Eun Jeong Park, Motomu Shimaoka, Ryuji Kaku

Abstract read
In one paragraph

Article in Journal of intensive care, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

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3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

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5 · Who and what money

Authors and funding

9 authors.

Shungo Takeuchi *Department of Anesthesiology, Mie University Graduate School of Medicine, Mie University, Tsu, Japan. stakeuchi@med.mie-u.ac.jp.
Eiji Kawamoto *Department of Anesthesiology, Mie University Graduate School of Medicine, Mie University, Tsu, Japan.
Takashi MatsusakiDepartment of Anesthesiology, Mie University Graduate School of Medicine, Mie University, Tsu, Japan.
Daisuke OnoDepartment of Anesthesiology, Mie University Graduate School of Medicine, Mie University, Tsu, Japan.
Yosuke SakakuraDepartment of Anesthesiology, Mie University Graduate School of Medicine, Mie University, Tsu, Japan.
Arong GaowaDepartment of Molecular Pathobiology and Cell Adhesion Biology, Mie University Graduate School of Medicine, Mie University, Tsu, Japan.
Eun Jeong ParkDepartment of Molecular Pathobiology and Cell Adhesion Biology, Mie University Graduate School of Medicine, Mie University, Tsu, Japan.
Motomu ShimaokaDepartment of Molecular Pathobiology and Cell Adhesion Biology, Mie University Graduate School of Medicine, Mie University, Tsu, Japan.
Ryuji KakuDepartment of Anesthesiology, Mie University Graduate School of Medicine, Mie University, Tsu, Japan.

Funding

Japan Society for the Promotion of Science JP24K02546Japan Society for the Promotion of Science JP25K12167
6 · The paper itself

Abstract

backgroundPersistent immune checkpoint activation is a recognized feature of critical coronavirus disease 2019 (COVID-19). However, the temporal behavior and clinical utility of soluble programmed death-ligand 1 (sPD-L1) remain unclear. We investigated the longitudinal changes in sPD-L1, its relationship with organ dysfunction markers, and their prognostic value when combined with machine learning (ML) models.

methodsIn this single-center observational study, we included 40 adults with severe COVID-19 pneumonia admitted to the intensive care unit (ICU) (April 2021-December 2022), and 23 healthy volunteers as controls. We measured plasma sPD-L1 on ICU day 1, 5, 7, 14, and 21. Routine biochemistry, complete blood counts, and arterial blood gas analyses were conducted in parallel. Cox regression was used to identify independent predictors of hospital mortality, the primary outcome. Eight ML classifiers were trained using admission variables and sPD-L1 levels from ICU day 1, 5, and 7. Discrimination was assessed using stratified fivefold cross-validation, and feature importance was evaluated using Shapley Additive Explanations (SHAP).

resultsOf 40 patients, 10 died during hospitalization. Overall, sPD-L1 levels declined during the ICU stay but remained persistently high in non-survivors. ICU day 5 and 7 values differed significantly between survivors and non-survivors (p = 0.023 and 0.001, respectively). In multivariable Cox analysis, ICU day 7 sPD-L1 levels and arterial lactate levels on admission independently predicted mortality. ICU day 7 sPD-L1 levels correlated positively with creatinine, C-reactive protein, and fibrinogen levels (all p < 0.05) in cross-sectional correlation analyses. Among ML models, the support vector machine achieved the highest discriminative accuracy (mean area under the curve = 0.917). ICU day 5 sPD-L1 was designated as the primary predictor of mortality based on SHAP analysis, with lactate contributing minimally.

conclusionSustained sPD-L1 elevation during the first ICU week is strongly associated with early organ dysfunction and independently predicts death in critical COVID-19. Incorporating serial sPD-L1 measurements into bedside ML models significantly enhances risk discrimination. These findings support sPD-L1 as an integrative biomarker of the immune-renal-coagulation interplay, warranting validation in larger multicenter cohorts and exploration as a potential companion marker for immune-modulatory interventions.

Indexed as

COVID-19ICU mortalityImmune checkpointsMachine learningOrgan dysfunctionSoluble PD-L1

Identifiers

PMID41491535
PMCPMC12870821

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.